'data flow' 검색 결과

Download genome assemblies, gene records, and ortholog data from NCBI using the modern Datasets v2 CLI (replaces assembly_summary.txt scraping and many EFetch workflows). Use when bulk-pulling genome assemblies, gene metadata across species, ortholog sets, or BLAST databases; when E-utilities are too slow for genome-scale work; or when automatic checksum verification, parallel download, and clean accession-driven retrieval are required. Encodes the JSON-lines output format, dataformat conversion, --dehydrated for cloud workflows, and when Datasets is/isn't the right tool.

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Defines cell populations in flow and spectral cytometry through manual gates (rectangle, polygon, quadrant, boolean) and reproducible automated gating (openCyto gating templates, flowDensity data-driven thresholds, flowClust model-based gates), organized as a hierarchical GatingSet (flowWorkspace) and round-tripped with FlowJo via CytoML. Covers the canonical gate order (time -> debris -> singlets -> live -> lineage), FMO-vs-isotype boundary setting, gate-order dependence and recompute semantics, rare-event/MRD gating, and per-population statistics. Use when building a gating strategy, automating a manual FlowJo scheme across samples, choosing manual vs data-driven gates, or extracting population frequencies.

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Download genome assemblies, gene records, and ortholog data from NCBI using the modern Datasets v2 CLI (replaces assembly_summary.txt scraping and many EFetch workflows). Use when bulk-pulling genome assemblies, gene metadata across species, ortholog sets, or BLAST databases; when E-utilities are too slow for genome-scale work; or when automatic checksum verification, parallel download, and clean accession-driven retrieval are required. Encodes the JSON-lines output format, dataformat conversion, --dehydrated for cloud workflows, and when Datasets is/isn't the right tool.

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Kotlin 코드 작성, 검토, 리팩터링을 돕습니다. 널 처리, 불변 데이터, 비동기 작업, 타입 안전한 설정을 점검해 읽기 쉽고 유지보수하기 좋은 구조로 정리합니다.

• Kotlin 널 처리 점검
• 가변 데이터와 깊은 중첩 정리
• 코루틴과 Flow 비동기 처리 설계

대량 데이터 수집, 과거 데이터 보충, 변환, 동기화의 지연을 단계별로 조사합니다. 처리 방식을 비교해 속도를 높이고 행 수, 파일 수, 시각의 일치 여부를 확인합니다.

• 대량 데이터 수집과 내보내기 가속
• 과거 데이터 보충 시간 단축
• 배치 크기와 병렬 작업 수 비교

앱이나 에이전트에 Firecrawl의 웹 검색, 페이지 내용 수집, 구조화된 정보 추출, 브라우저 조작 기능을 추가하도록 선택부터 설정, 구현, 동작 확인까지 안내합니다.

• 앱에 웹 검색 추가
• 알고 있는 URL에서 구조화된 데이터 추출
• 클릭과 양식 입력을 처리 흐름에 추가

Excel과 CSV 데이터를 살펴보고 집계, 통계 요약, 여러 파일의 결합을 수행합니다. 월별 비교와 순위를 지원하며 결과를 CSV, JSON, Markdown으로 출력합니다.

• 월별 매출과 인기 상품 비교
• 주문 데이터와 고객 정보 결합
• 결측값과 기초 통계 확인

Develop machine learning models from local data, covering data preparation, model selection, training, evaluation, and deployment preparation. Use it to build workflows for tasks such as predicting customer churn or classifying images.

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• Building a model to predict customer churn
• Training a neural network for image classification
• Preparing missing values and categorical data for training

Train, deploy, and manage neural networks with Flow Nexus in isolated E2B environments. Use custom architectures or ready-made templates, run predictions, monitor distributed training across multiple environments, and evaluate models before sharing them.

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• Train an image classification model
• Forecast time series with LSTM
• Deploy a sentiment analysis template

Builds a 30/60/90-day cash flow forecast from accounting records, payment services, or CSV data. Uses historical payment timing to estimate uncertainty, flags specific cash risks, and delivers a chat summary, forecast page, and downloadable Excel workbook.

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• Check whether cash will cover the next payroll
• Review cash flow across three 30-day windows
• Identify invoices whose late payment could cause a shortfall

Build Python applications that collect live cryptocurrency prices, trades, and order books with Cryptofeed. Covers consistent data formats across exchanges, price comparisons, and writing incoming data to storage systems for monitoring and analysis.

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• Monitoring prices across cryptocurrency exchanges
• Analyzing order book depth
• Analyzing executed trade flows

Audit one datafusion-spark function against Apache Spark 4.2.0. Compare source code, accepted types, and edge cases, verify expected results with PySpark, then fix differences and record remaining gaps as issues and disabled tests.

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• Auditing a function for Spark compatibility
• Checking NULL, overflow, and ANSI mode behavior
• Finding missing SQL test coverage

Scans code for security vulnerabilities with CodeQL by tracing data across functions. Builds and checks analysis databases, models project-specific APIs, and saves findings in SARIF, a structured results format, with broad or focused scan options.

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• Scanning a codebase for security vulnerabilities
• Tracing untrusted input through custom APIs
• Choosing broad or high-precision security scans

Build or review quality control workflows for single-cell and single-nucleus RNA sequencing data. Select filters from observed data, detect doublets, assign cell types, and create UMAP plots that summarize cell relationships, while recording analysis decisions and outputs.

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• Choosing quality filters from dataset distributions
• Detecting doublets and checking ambient RNA
• Assigning cell types using references or marker genes

Turn descriptions or code analysis into Mermaid diagrams for workflows, system architecture, and database relationships. Check diagram syntax and export SVG, PNG, or text-based ASCII output, with themes and batch rendering options.

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• Visualizing system architecture
• Documenting API request sequences
• Drawing database relationships

Generate ASP.NET Core pages, components, or API endpoints for creating, reading, updating, and deleting database records. Follow the project's existing styling and code conventions, with database migrations and verification of the generated functionality.

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• Add database-backed record management pages
• Generate Minimal API endpoints for a model
• Create Blazor forms that match existing styling

Build research workflows that regenerate results from raw data with a single command. Organize project files, configure workflow tools, manage software environments and data versions, and prepare replication packages for journal submission.

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• Setting up folders for a new research project
• Automating analysis and figure generation
• Debugging results that differ across computers

Explore scientific data files to understand their structure, statistical patterns, and quality. References cover over 200 formats, with workflows for producing Markdown reports that summarize findings and recommend preprocessing, visualizations, and further analysis.

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• Checking missing values and outliers in experimental tables
• Examining sequence lengths and quality scores in FASTQ files
• Inspecting microscopy image dimensions and metadata

Turn research about an existing app into a technical plan for a new implementation. Choose technologies, define database tables and access rules, map API routes, and order development around a small, complete core workflow.

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• Choose technologies for an alternative app
• Design database tables, indexes, and access rules
• Map API routes and scheduled background jobs

Use when running video data augmentation and auto-labeling workflows on OSMO: flow selection, preflight, submit-time interpolation, monitoring, and output retrieval. Trigger keywords: video data augmentation, data enrichment, auto labeling, VDA demo, OSMO workflow, pseudo labeling.

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This skill should be considered when you need to answer reflow machine maintenance questions or provide detailed guidance based on thermocouple data, MES data or defect data and reflow technical handbooks. This skill covers how to obtain important concepts, calculations, definitions, thresholds, and others from the handbook and how to do cross validations between handbook and datasets.

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Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.

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Expert knowledge for Azure Databricks development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using Unity Catalog, Lakehouse/Lakeflow pipelines, AI Runtime/MLflow, model serving, or agents on Databricks, and other Azure Databricks related development tasks. Not for Azure Synapse Analytics (use azure-synapse-analytics), Azure HDInsight (use azure-hdinsight), Azure Machine Learning (use azure-machine-learning), Azure Data Factory (use azure-data-factory).

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Meta-skill for designing orchestrator+phases structured workflow skills. Creates SKILL.md coordinator with progressive phase loading, TodoWrite patterns, and data flow. Triggers on "design workflow skill", "create workflow skill", "workflow skill designer".

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